Ares: Approximate Representations via Efficient Sparsification -- A Stateless Approach through Polynomial Homomorphism
This provides a scalable compression method for streaming or infinite datasets, though it appears incremental as it builds on existing representation techniques.
The paper tackles the problem of efficient compression for high-dimensional data by introducing a stateless framework using polynomial representations, achieving high compression ratios without compromising reconstruction accuracy in experiments.
The increasing prevalence of high-dimensional data demands efficient and scalable compression methods to support modern applications. However, existing techniques like PCA and Autoencoders often rely on auxiliary metadata or intricate architectures, limiting their practicality for streaming or infinite datasets. In this paper, we introduce a stateless compression framework that leverages polynomial representations to achieve compact, interpretable, and scalable data reduction. By eliminating the need for auxiliary data, our method supports direct algebraic operations in the compressed domain while minimizing error growth during computations. Through extensive experiments on synthetic and real-world datasets, we show that our approach achieves high compression ratios without compromising reconstruction accuracy, all while maintaining simplicity and scalability.